In clinical examinations and diagnoses, low-dose computed tomography (LDCT) is crucial for minimizing health risks compared with normal-dose computed tomography (NDCT). However, reducing the radiation dose compromises the signal-to-noise ratio, leading to degraded quality of CT images. To address this, we analyze LDCT denoising task based on experimental results from the frequency perspective, and then introduce a novel self-supervised CT image denoising method called WIA-LD2ND, only using NDCT data. The proposed WIA-LD2ND comprises two modules: Wavelet-based Image Alignment (WIA) and Frequency-Aware Multi-scale Loss (FAM). First, WIA is introduced to align NDCT with LDCT by mainly adding noise to the high-frequency components, which is the main difference between LDCT and NDCT. Second, to better capture high-frequency components and detailed information, Frequency-Aware Multi-scale Loss (FAM) is proposed by effectively utilizing multi-scale feature space. Extensive experiments on two public LDCT denoising datasets demonstrate that our WIA-LD2ND, only uses NDCT, outperforms existing several state-of-the-art weakly-supervised and self-supervised methods.

翻译：在临床检查与诊断中，低剂量计算机断层扫描（LDCT）相较于常规剂量计算机断层扫描（NDCT）对降低健康风险至关重要。然而，辐射剂量的降低会牺牲信噪比，导致CT图像质量下降。针对这一问题，我们从频域角度基于实验结果分析了LDCT去噪任务，并提出了一种仅使用NDCT数据的新型自监督CT图像去噪方法——WIA-LD2ND。该方法包含两个模块：基于小波的图像对齐（WIA）和频域感知多尺度损失（FAM）。首先，WIA通过主要向高频分量（LDCT与NDCT的主要差异所在）添加噪声，实现NDCT与LDCT的对齐；其次，为更好捕捉高频分量和细节信息，提出频域感知多尺度损失（FAM），有效利用多尺度特征空间。在两个公开LDCT去噪数据集上的大量实验表明，仅使用NDCT数据的WIA-LD2ND方法优于现有多种先进的弱监督和自监督方法。